1 | #TODO: Use simview to generate P(r) and I(q) pairs in sansview. |
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2 | # Make sure the option of saving each curve is available |
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3 | # Use the I(q) curve as input and compare the output to P(r) |
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4 | |
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5 | import os |
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6 | import wx |
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7 | from sans.guitools.plottables import Data1D, Theory1D |
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8 | from sans.guicomm.events import NewPlotEvent, StatusEvent |
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9 | import math, numpy |
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10 | from sans.pr.invertor import Invertor |
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11 | |
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12 | class Plugin: |
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13 | |
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14 | def __init__(self): |
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15 | ## Plug-in name |
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16 | self.sub_menu = "Pr inversion" |
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17 | |
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18 | ## Reference to the parent window |
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19 | self.parent = None |
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20 | |
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21 | ## Simulation window manager |
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22 | self.simview = None |
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23 | |
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24 | ## List of panels for the simulation perspective (names) |
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25 | self.perspective = [] |
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26 | |
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27 | ## State data |
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28 | self.alpha = 0.0001 |
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29 | self.nfunc = 10 |
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30 | self.max_length = 140.0 |
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31 | self.q_min = None |
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32 | self.q_max = None |
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33 | ## Remember last plottable processed |
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34 | self.last_data = "sphere_60_q0_2.txt" |
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35 | ## Time elapsed for last computation [sec] |
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36 | # Start with a good default |
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37 | self.elapsed = 0.022 |
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38 | |
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39 | ## Current invertor |
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40 | self.invertor = None |
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41 | ## Calculation thread |
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42 | self.calc_thread = None |
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43 | ## Estimation thread |
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44 | self.estimation_thread = None |
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45 | ## Result panel |
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46 | self.control_panel = None |
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47 | ## Currently views plottable |
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48 | self.current_plottable = None |
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49 | |
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50 | def populate_menu(self, id, owner): |
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51 | """ |
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52 | Create a menu for the plug-in |
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53 | """ |
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54 | import wx |
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55 | shapes = wx.Menu() |
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56 | |
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57 | id = wx.NewId() |
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58 | shapes.Append(id, '&Sphere test') |
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59 | wx.EVT_MENU(owner, id, self._fit_pr) |
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60 | |
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61 | return [(id, shapes, "P(r)")] |
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62 | |
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63 | def help(self, evt): |
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64 | """ |
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65 | Show a general help dialog. |
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66 | TODO: replace the text with a nice image |
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67 | """ |
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68 | from inversion_panel import HelpDialog |
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69 | dialog = HelpDialog(None, -1) |
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70 | if dialog.ShowModal() == wx.ID_OK: |
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71 | dialog.Destroy() |
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72 | else: |
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73 | dialog.Destroy() |
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74 | |
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75 | def _fit_pr(self, evt): |
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76 | from sans.pr.invertor import Invertor |
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77 | import numpy |
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78 | import pylab |
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79 | import math |
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80 | from sans.guicomm.events import NewPlotEvent |
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81 | from sans.guitools.plottables import Data1D, Theory1D |
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82 | |
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83 | # Generate P(r) for sphere |
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84 | radius = 60.0 |
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85 | d_max = 2*radius |
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86 | |
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87 | |
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88 | r = pylab.arange(0.01, d_max, d_max/51.0) |
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89 | M = len(r) |
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90 | y = numpy.zeros(M) |
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91 | pr_err = numpy.zeros(M) |
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92 | |
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93 | sum = 0.0 |
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94 | for j in range(M): |
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95 | value = self.pr_theory(r[j], radius) |
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96 | sum += value |
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97 | y[j] = value |
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98 | pr_err[j] = math.sqrt(y[j]) |
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99 | |
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100 | |
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101 | y = y/sum*d_max/len(r) |
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102 | |
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103 | |
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104 | |
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105 | # Perform fit |
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106 | pr = Invertor() |
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107 | pr.d_max = d_max |
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108 | pr.alpha = 0 |
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109 | pr.x = r |
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110 | pr.y = y |
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111 | pr.err = pr_err |
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112 | out, cov = pr.pr_fit() |
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113 | for i in range(len(out)): |
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114 | print "%g +- %g" % (out[i], math.sqrt(cov[i][i])) |
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115 | |
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116 | |
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117 | # Show input P(r) |
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118 | new_plot = Data1D(pr.x, pr.y, pr.err) |
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119 | new_plot.name = "P_{obs}(r)" |
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120 | new_plot.xaxis("\\rm{r}", 'A') |
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121 | new_plot.yaxis("\\rm{P(r)} ","cm^{-3}") |
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122 | wx.PostEvent(self.parent, NewPlotEvent(plot=new_plot, title="Pr")) |
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123 | |
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124 | # Show P(r) fit |
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125 | self.show_pr(out, pr) |
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126 | |
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127 | # Show I(q) fit |
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128 | q = pylab.arange(0.001, 0.1, 0.01/51.0) |
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129 | self.show_iq(out, pr, q) |
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130 | |
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131 | |
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132 | def show_shpere(self, x, radius=70.0, x_range=70.0): |
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133 | import numpy |
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134 | import pylab |
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135 | import math |
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136 | from sans.guicomm.events import NewPlotEvent |
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137 | from sans.guitools.plottables import Data1D, Theory1D |
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138 | # Show P(r) |
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139 | y_true = numpy.zeros(len(x)) |
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140 | |
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141 | sum_true = 0.0 |
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142 | for i in range(len(x)): |
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143 | y_true[i] = self.pr_theory(x[i], radius) |
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144 | sum_true += y_true[i] |
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145 | |
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146 | y_true = y_true/sum_true*x_range/len(x) |
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147 | |
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148 | # Show the theory P(r) |
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149 | new_plot = Theory1D(x, y_true) |
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150 | new_plot.name = "P_{true}(r)" |
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151 | new_plot.xaxis("\\rm{r}", 'A') |
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152 | new_plot.yaxis("\\rm{P(r)} ","cm^{-3}") |
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153 | |
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154 | |
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155 | #Put this call in plottables/guitools |
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156 | wx.PostEvent(self.parent, NewPlotEvent(plot=new_plot, title="Sphere P(r)")) |
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157 | |
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158 | def show_iq(self, out, pr, q=None): |
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159 | import numpy |
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160 | import pylab |
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161 | import math |
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162 | from sans.guicomm.events import NewPlotEvent |
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163 | from sans.guitools.plottables import Data1D, Theory1D |
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164 | |
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165 | qtemp = pr.x |
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166 | if not q==None: |
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167 | qtemp = q |
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168 | |
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169 | # Make a plot |
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170 | maxq = -1 |
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171 | for q_i in qtemp: |
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172 | if q_i>maxq: |
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173 | maxq=q_i |
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174 | |
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175 | minq = 0.001 |
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176 | |
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177 | # Check for user min/max |
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178 | if not pr.q_min==None: |
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179 | minq = pr.q_min |
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180 | if not pr.q_max==None: |
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181 | maxq = pr.q_max |
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182 | |
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183 | x = pylab.arange(minq, maxq, maxq/301.0) |
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184 | y = numpy.zeros(len(x)) |
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185 | err = numpy.zeros(len(x)) |
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186 | for i in range(len(x)): |
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187 | value = pr.iq(out, x[i]) |
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188 | y[i] = value |
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189 | try: |
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190 | err[i] = math.sqrt(math.fabs(value)) |
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191 | except: |
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192 | err[i] = 1.0 |
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193 | print "Error getting error", value, x[i] |
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194 | |
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195 | new_plot = Theory1D(x, y) |
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196 | new_plot.name = "I_{fit}(q)" |
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197 | new_plot.xaxis("\\rm{Q}", 'A^{-1}') |
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198 | new_plot.yaxis("\\rm{Intensity} ","cm^{-1}") |
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199 | #new_plot.group_id = "test group" |
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200 | wx.PostEvent(self.parent, NewPlotEvent(plot=new_plot, title="Iq")) |
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201 | |
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202 | |
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203 | |
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204 | |
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205 | def show_pr(self, out, pr, cov=None): |
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206 | import numpy |
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207 | import pylab |
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208 | import math |
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209 | from sans.guicomm.events import NewPlotEvent |
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210 | from sans.guitools.plottables import Data1D, Theory1D |
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211 | |
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212 | # Show P(r) |
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213 | x = pylab.arange(0.0, pr.d_max, pr.d_max/51.0) |
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214 | |
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215 | y = numpy.zeros(len(x)) |
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216 | dy = numpy.zeros(len(x)) |
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217 | y_true = numpy.zeros(len(x)) |
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218 | |
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219 | sum = 0.0 |
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220 | for i in range(len(x)): |
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221 | if cov==None: |
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222 | value = pr.pr(out, x[i]) |
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223 | else: |
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224 | (value, dy[i]) = pr.pr_err(out, cov, x[i]) |
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225 | sum += value |
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226 | y[i] = value |
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227 | |
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228 | y = y/sum*pr.d_max/len(x) |
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229 | dy = dy/sum*pr.d_max/len(x) |
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230 | |
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231 | if cov==None: |
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232 | new_plot = Theory1D(x, y) |
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233 | else: |
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234 | new_plot = Data1D(x, y, dy=dy) |
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235 | new_plot.name = "P_{fit}(r)" |
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236 | new_plot.xaxis("\\rm{r}", 'A') |
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237 | new_plot.yaxis("\\rm{P(r)} ","cm^{-3}") |
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238 | |
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239 | wx.PostEvent(self.parent, NewPlotEvent(plot=new_plot, title="P(r) fit")) |
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240 | |
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241 | return x, pr.d_max |
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242 | |
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243 | |
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244 | def choose_file(self): |
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245 | """ |
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246 | |
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247 | """ |
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248 | #TODO: this should be in a common module |
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249 | return self.parent.choose_file() |
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250 | |
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251 | def load(self, path = "sphere_test_data.txt"): |
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252 | import numpy, math, sys |
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253 | # Read the data from the data file |
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254 | data_x = numpy.zeros(0) |
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255 | data_y = numpy.zeros(0) |
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256 | data_err = numpy.zeros(0) |
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257 | if not path == None: |
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258 | input_f = open(path,'r') |
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259 | buff = input_f.read() |
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260 | lines = buff.split('\n') |
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261 | for line in lines: |
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262 | try: |
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263 | toks = line.split() |
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264 | x = float(toks[0]) |
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265 | y = float(toks[1]) |
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266 | data_x = numpy.append(data_x, x) |
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267 | data_y = numpy.append(data_y, y) |
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268 | try: |
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269 | scale = 0.05/math.sqrt(data_x[0]) |
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270 | except: |
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271 | scale = 1.0 |
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272 | #data_err = numpy.append(data_err, 10.0*math.sqrt(y)+1000.0) |
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273 | data_err = numpy.append(data_err, scale*math.sqrt(y)) |
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274 | except: |
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275 | print "Error reading line: ", line |
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276 | print sys.exc_value |
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277 | |
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278 | print "Lines read:", len(data_x) |
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279 | return data_x, data_y, data_err |
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280 | |
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281 | def pr_theory(self, r, R): |
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282 | """ |
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283 | |
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284 | """ |
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285 | if r<=2*R: |
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286 | return 12.0* ((0.5*r/R)**2) * ((1.0-0.5*r/R)**2) * ( 2.0 + 0.5*r/R ) |
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287 | else: |
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288 | return 0.0 |
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289 | |
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290 | def get_context_menu(self, plot_id=None): |
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291 | """ |
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292 | Get the context menu items available for P(r) |
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293 | @param plot_id: Unique ID of a plot, so that we can recognize those |
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294 | that we created |
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295 | @return: a list of menu items with call-back function |
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296 | """ |
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297 | return [["Compute P(r)", "Compute P(r) from distribution", self._on_context_inversion]] |
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298 | |
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299 | |
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300 | def start_thread(self): |
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301 | from pr_thread import CalcPr |
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302 | from copy import deepcopy |
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303 | |
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304 | # If a thread is already started, stop it |
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305 | if self.calc_thread != None and self.calc_thread.isrunning(): |
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306 | self.calc_thread.stop() |
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307 | |
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308 | pr = self.pr.clone() |
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309 | self.calc_thread = CalcPr(pr, self.nfunc, error_func=self._thread_error, completefn=self._completed, updatefn=None) |
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310 | self.calc_thread.queue() |
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311 | self.calc_thread.ready(2.5) |
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312 | |
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313 | def _thread_error(self, error): |
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314 | wx.PostEvent(self.parent, StatusEvent(status=error)) |
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315 | |
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316 | def _estimate_completed(self, alpha, message, elapsed): |
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317 | """ |
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318 | Parameter estimation completed, |
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319 | display the results to the user |
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320 | @param alpha: estimated best alpha |
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321 | @param elapsed: computation time |
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322 | """ |
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323 | # Save useful info |
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324 | self.elapsed = elapsed |
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325 | self.control_panel.alpha_estimate = alpha |
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326 | if not message==None: |
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327 | wx.PostEvent(self.parent, StatusEvent(status=str(message))) |
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328 | |
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329 | def _completed(self, out, cov, pr, elapsed): |
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330 | """ |
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331 | Method called with the results when the inversion |
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332 | is done |
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333 | |
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334 | @param out: output coefficient for the base functions |
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335 | @param cov: covariance matrix |
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336 | @param pr: Invertor instance |
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337 | @param elapsed: time spent computing |
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338 | """ |
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339 | # Save useful info |
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340 | self.elapsed = elapsed |
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341 | message = "Computation completed in %g seconds [chi2=%g]" % (elapsed, pr.chi2) |
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342 | wx.PostEvent(self.parent, StatusEvent(status=message)) |
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343 | |
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344 | # Show result on control panel |
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345 | self.control_panel.chi2 = pr.chi2 |
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346 | self.control_panel.elapsed = elapsed |
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347 | self.control_panel.oscillation = pr.oscillations(out) |
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348 | #print "OSCILL", pr.oscillations(out) |
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349 | print "PEAKS:", pr.get_peaks(out) |
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350 | |
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351 | for i in range(len(out)): |
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352 | try: |
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353 | print "%d: %g +- %g" % (i, out[i], math.sqrt(math.fabs(cov[i][i]))) |
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354 | except: |
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355 | print "%d: %g +- ?" % (i, out[i]) |
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356 | |
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357 | # Make a plot of I(q) data |
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358 | new_plot = Data1D(self.pr.x, self.pr.y, self.pr.err) |
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359 | new_plot.name = "I_{obs}(q)" |
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360 | new_plot.xaxis("\\rm{Q}", 'A^{-1}') |
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361 | new_plot.yaxis("\\rm{Intensity} ","cm^{-1}") |
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362 | #new_plot.group_id = "test group" |
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363 | wx.PostEvent(self.parent, NewPlotEvent(plot=new_plot, title="Iq")) |
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364 | |
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365 | # Show I(q) fit |
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366 | self.show_iq(out, self.pr) |
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367 | |
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368 | # Show P(r) fit |
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369 | x_values, x_range = self.show_pr(out, self.pr) |
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370 | |
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371 | # Popup result panel |
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372 | #result_panel = InversionResults(self.parent, -1, style=wx.RAISED_BORDER) |
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373 | |
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374 | def setup_plot_inversion(self, alpha, nfunc, d_max, q_min=None, q_max=None): |
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375 | self.alpha = alpha |
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376 | self.nfunc = nfunc |
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377 | self.max_length = d_max |
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378 | self.q_min = q_min |
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379 | self.q_max = q_max |
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380 | |
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381 | self._create_plot_pr() |
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382 | self.perform_inversion() |
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383 | |
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384 | def estimate_plot_inversion(self, alpha, nfunc, d_max, q_min=None, q_max=None): |
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385 | self.alpha = alpha |
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386 | self.nfunc = nfunc |
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387 | self.max_length = d_max |
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388 | self.q_min = q_min |
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389 | self.q_max = q_max |
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390 | |
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391 | self._create_plot_pr() |
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392 | self.perform_estimate() |
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393 | |
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394 | def _create_plot_pr(self): |
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395 | """ |
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396 | Create and prepare invertor instance from |
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397 | a plottable data set. |
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398 | @param path: path of the file to read in |
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399 | """ |
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400 | # Get the data from the chosen data set and perform inversion |
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401 | pr = Invertor() |
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402 | pr.d_max = self.max_length |
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403 | pr.alpha = self.alpha |
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404 | pr.q_min = self.q_min |
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405 | pr.q_max = self.q_max |
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406 | pr.x = self.current_plottable.x |
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407 | pr.y = self.current_plottable.y |
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408 | |
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409 | # Fill in errors if none were provided |
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410 | if self.current_plottable.dy == None: |
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411 | print "no error", self.current_plottable.name |
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412 | y = numpy.zeros(len(pr.y)) |
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413 | for i in range(len(pr.y)): |
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414 | y[i] = math.sqrt(pr.y[i]) |
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415 | pr.err = y |
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416 | else: |
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417 | pr.err = self.current_plottable.dy |
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418 | |
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419 | self.pr = pr |
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420 | |
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421 | |
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422 | def setup_file_inversion(self, alpha, nfunc, d_max, path, q_min=None, q_max=None): |
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423 | self.alpha = alpha |
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424 | self.nfunc = nfunc |
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425 | self.max_length = d_max |
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426 | self.q_min = q_min |
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427 | self.q_max = q_max |
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428 | |
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429 | self._create_file_pr(path) |
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430 | |
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431 | self.perform_inversion() |
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432 | |
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433 | def estimate_file_inversion(self, alpha, nfunc, d_max, path, q_min=None, q_max=None): |
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434 | self.alpha = alpha |
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435 | self.nfunc = nfunc |
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436 | self.max_length = d_max |
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437 | self.q_min = q_min |
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438 | self.q_max = q_max |
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439 | |
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440 | if self._create_file_pr(path): |
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441 | self.perform_estimate() |
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442 | |
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443 | def _create_file_pr(self, path): |
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444 | """ |
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445 | Create and prepare invertor instance from |
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446 | a file data set. |
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447 | @param path: path of the file to read in |
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448 | """ |
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449 | # Load data |
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450 | if os.path.isfile(path): |
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451 | x, y, err = self.load(path) |
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452 | |
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453 | # Get the data from the chosen data set and perform inversion |
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454 | pr = Invertor() |
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455 | pr.d_max = self.max_length |
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456 | pr.alpha = self.alpha |
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457 | pr.q_min = self.q_min |
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458 | pr.q_max = self.q_max |
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459 | pr.x = x |
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460 | pr.y = y |
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461 | pr.err = err |
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462 | |
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463 | self.pr = pr |
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464 | return True |
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465 | return False |
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466 | |
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467 | def perform_estimate(self): |
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468 | from pr_thread import EstimatePr |
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469 | from copy import deepcopy |
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470 | |
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471 | wx.PostEvent(self.parent, StatusEvent(status='')) |
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472 | # If a thread is already started, stop it |
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473 | if self.estimation_thread != None and self.estimation_thread.isrunning(): |
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474 | self.estimation_thread.stop() |
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475 | |
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476 | pr = self.pr.clone() |
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477 | self.estimation_thread = EstimatePr(pr, self.nfunc, error_func=self._thread_error, |
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478 | completefn = self._estimate_completed, |
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479 | updatefn = None) |
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480 | self.estimation_thread.queue() |
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481 | self.estimation_thread.ready(2.5) |
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482 | |
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483 | def perform_inversion(self): |
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484 | |
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485 | # Time estimate |
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486 | #estimated = self.elapsed*self.nfunc**2 |
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487 | message = "Computation time may take up to %g seconds" % self.elapsed |
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488 | wx.PostEvent(self.parent, StatusEvent(status=message)) |
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489 | |
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490 | # Start inversion thread |
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491 | self.start_thread() |
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492 | return |
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493 | |
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494 | out, cov = self.pr.lstsq(self.nfunc) |
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495 | |
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496 | # Save useful info |
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497 | self.elapsed = self.pr.elapsed |
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498 | |
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499 | for i in range(len(out)): |
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500 | try: |
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501 | print "%d: %g +- %g" % (i, out[i], math.sqrt(math.fabs(cov[i][i]))) |
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502 | except: |
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503 | print "%d: %g +- ?" % (i, out[i]) |
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504 | |
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505 | |
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506 | |
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507 | # Make a plot of I(q) data |
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508 | new_plot = Data1D(self.pr.x, self.pr.y, dy=self.pr.err) |
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509 | new_plot.name = "I_{obs}(q)" |
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510 | new_plot.xaxis("\\rm{Q}", 'A^{-1}') |
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511 | new_plot.yaxis("\\rm{Intensity} ","cm^{-1}") |
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512 | wx.PostEvent(self.parent, NewPlotEvent(plot=new_plot, title="Iq")) |
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513 | |
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514 | # Show I(q) fit |
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515 | self.show_iq(out, self.pr) |
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516 | |
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517 | # Show P(r) fit |
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518 | x_values, x_range = self.show_pr(out, self.pr, cov=cov) |
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519 | |
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520 | |
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521 | |
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522 | |
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523 | def _on_context_inversion(self, event): |
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524 | panel = event.GetEventObject() |
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525 | |
---|
526 | from inversion_panel import InversionDlg |
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527 | |
---|
528 | # If we have more than one displayed plot, make the user choose |
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529 | if len(panel.plots)>1: |
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530 | dialog = InversionDlg(None, -1, "P(r) Inversion", panel.plots, pars=False) |
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531 | dialog.set_content(self.last_data, self.nfunc, self.alpha, self.max_length) |
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532 | if dialog.ShowModal() == wx.ID_OK: |
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533 | dataset = dialog.get_content() |
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534 | dialog.Destroy() |
---|
535 | else: |
---|
536 | dialog.Destroy() |
---|
537 | return |
---|
538 | elif len(panel.plots)==1: |
---|
539 | dataset = panel.plots.keys()[0] |
---|
540 | else: |
---|
541 | print "Error: No data is available" |
---|
542 | return |
---|
543 | |
---|
544 | # Store a reference to the current plottable |
---|
545 | self.current_plottable = panel.plots[dataset] |
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546 | self.control_panel.plotname = dataset |
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547 | self.control_panel.nfunc = self.nfunc |
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548 | self.control_panel.d_max = self.max_length |
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549 | self.control_panel.alpha = self.alpha |
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550 | self.parent.set_perspective(self.perspective) |
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551 | |
---|
552 | def get_panels(self, parent): |
---|
553 | """ |
---|
554 | Create and return a list of panel objects |
---|
555 | """ |
---|
556 | from inversion_panel import InversionControl |
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557 | |
---|
558 | self.parent = parent |
---|
559 | self.control_panel = InversionControl(self.parent, -1, style=wx.RAISED_BORDER) |
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560 | self.control_panel.set_manager(self) |
---|
561 | self.control_panel.nfunc = self.nfunc |
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562 | self.control_panel.d_max = self.max_length |
---|
563 | self.control_panel.alpha = self.alpha |
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564 | |
---|
565 | self.perspective = [] |
---|
566 | self.perspective.append(self.control_panel.window_name) |
---|
567 | return [self.control_panel] |
---|
568 | |
---|
569 | def get_perspective(self): |
---|
570 | """ |
---|
571 | Get the list of panel names for this perspective |
---|
572 | """ |
---|
573 | return self.perspective |
---|
574 | |
---|
575 | def on_perspective(self, event): |
---|
576 | """ |
---|
577 | Call back function for the perspective menu item. |
---|
578 | We notify the parent window that the perspective |
---|
579 | has changed. |
---|
580 | """ |
---|
581 | self.parent.set_perspective(self.perspective) |
---|
582 | |
---|
583 | def post_init(self): |
---|
584 | """ |
---|
585 | Post initialization call back to close the loose ends |
---|
586 | [Somehow openGL needs this call] |
---|
587 | """ |
---|
588 | self.parent.set_perspective(self.perspective) |
---|
589 | |
---|